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. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load
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of Electrical Engineering at TU/e is a globally recognised, leading scientific and applied research group focused on exploiting light for communication and quantum systems. We apply our knowledge in collaboration
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
following qualifications and experience will be considered during candidate assessment: Experience in Machine Learning, Deep Learning, reduced-order models, or physics-informed models; Knowledge of numerical
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to the development of techno-economic models, and supervise the PhD candidate in all research activities related to energy economics. The researcher will work closely with the principal investigator and the project
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 12 days ago
pathways in cellular and mouse models of metabolic diseases You will perform biochemical and cell biological characterization of protein localization and organelle interactions You will apply and further
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’, a consortium of scientists will combine technological, methodological and ecological knowledge to take a next step in using remote sensing techniques for ecological monitoring of ecosystems across
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combine insights from both engineering and social sciences as well as the humanities. TPM develops robust models and designs, is internationally oriented and has an extensive network of knowledge
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scientific and technical knowledge as evidenced by publications (if applicable) or successful projects in line with the job description.- Familiar with the latest in AI trends and models for development and
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contribute to the development and validation of computationally efficient motor-twin models for permanent magnet synchronous machines. The work will focus on machine modelling, parameter and state estimation